D465 Data Applications
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Free D465 Data Applications Questions
What is the primary function of prescriptive analytics in data science?
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To analyze historical data
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To recommend actions for desired outcomes
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To visualize data trends
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To clean and transform raw data
Explanation
Explanation:
Prescriptive analytics goes beyond descriptive and predictive analytics by recommending specific actions to achieve desired outcomes. It uses data, models, and algorithms to suggest the best course of action for decision-making. While analyzing historical data and visualizing trends are important, prescriptive analytics specifically focuses on providing actionable recommendations rather than just insights or forecasts.
Correct Answer:
To recommend actions for desired outcomes
In RStudio, the _____ is where you can find all the data you currently have loaded, organize it, and save it.
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Environment pane
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Plots pane
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Source editor pane
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R console pane
Explanation
Explanation:
The environment pane in RStudio displays all the objects, such as datasets, variables, and functions, that are currently loaded in the R session. It allows users to inspect the structure of their data, view summaries, and manage objects efficiently. Additionally, the environment pane supports saving objects to disk or removing them from memory, providing a central location for managing the data used in analysis. This makes it an essential feature for organizing and tracking the workspace.
Correct Answer:
Environment pane
A data analyst wants to perform an analysis and make it easy for colleagues to understand his process and update the analysis a year from now. Which tool is best to achieve this objective?
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PDF document
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Code chunks
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R Markdown
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Word Document
Explanation
Explanation:
R Markdown is the best tool for creating reproducible and dynamic reports. It allows analysts to combine narrative text, code chunks, and the resulting outputs (tables, plots, etc.) in a single document. This makes it easy for colleagues to follow the analysis workflow and update the report in the future, as the code can be re-run with new data without recreating the report from scratch. Unlike static PDFs or Word documents, R Markdown integrates code execution directly into the document.
Correct Answer:
R Markdown
Which function is used to add captions to a plot built with the ggplot2 package in R?
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labs()
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facet_grid()
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geom_line()
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aes()
Explanation
Explanation
Correct answer: (A.) labs()
In ggplot2, the labs() function is used to modify and add labels to a plot, including titles, subtitles, axis labels, and captions. Captions are specifically added using the caption argument inside labs(). The other functions serve different purposes: facet_grid() is used to create subplots based on categorical variables, geom_line() adds line geometry to a plot, and aes() defines aesthetic mappings such as x and y variables.
Explain how personalized medicine utilizes data science techniques to improve patient care. Which aspects of data science are most relevant to this application?
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It uses machine learning to predict disease outbreaks.
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It analyzes genetic information to tailor treatments to individual patients.
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It focuses on optimizing hospital operations through data analysis.
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It employs natural language processing to enhance patient communication.
Explanation
Explanation:
Personalized medicine leverages data science to provide individualized treatment plans based on a patient’s unique genetic, clinical, and lifestyle data. By applying machine learning and bioinformatics, data scientists can analyze vast amounts of genomic data to identify genetic variations linked to specific diseases or drug responses. This enables healthcare providers to predict how different patients will respond to treatments and choose therapies that are most effective for each individual. Key data science aspects in this field include predictive modeling, pattern recognition, and data integration from multiple biomedical sources, all of which contribute to more accurate diagnoses and better patient outcomes.
Correct Answer:
It analyzes genetic information to tailor treatments to individual patients
An analyst is working on a pivot table in Google Sheets and needs to figure out how many new products in each year earned less than $3 million. What does the analyst do to view these new products?
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Add a formula
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Add a calculated field
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Add a conditional filter
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Add a fill handle
Explanation
Explanation
Correct answer: (C.) Add a conditional filter
To view only specific subsets of data in a pivot table—such as new products with earnings below $3 million—the analyst applies a filter condition. A conditional filter allows the pivot table to restrict displayed results based on defined criteria, such as revenue thresholds or product attributes, making it possible to isolate and analyze only the relevant records. Calculated fields are used to create new computed metrics, formulas are general spreadsheet tools, and the fill handle is used for copying or extending patterns, none of which directly filter pivot table results.
Explain why programming is a critical skill in data science, particularly in the context of data manipulation and analysis.
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It allows for the creation of user interfaces.
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It enables the automation of repetitive tasks
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It provides tools for data visualization only.
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It is only necessary for statistical analysis
Explanation
Explanation:
Programming is essential in data science because it enables data scientists to efficiently manipulate, clean, and analyze large datasets. Through programming languages such as Python, R, or SQL, data scientists can automate repetitive tasks, perform complex calculations, and build scalable workflows for data processing. This skill also allows for integration of multiple data sources, the creation of reusable scripts, and the implementation of machine learning algorithms. Beyond automation, programming facilitates reproducibility and accuracy in data analysis, ensuring that insights and models are both reliable and adaptable to new data.
Correct Answer:
It enables the automation of repetitive tasks
What does the analyst use to convert the text to numeric values?
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VLOOKUP function
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COUNT function
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Absolute cell reference
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Conditional formatting
Explanation
Explanation
Correct answer: (A.) VLOOKUP function
When working in a spreadsheet like Google Sheets or Excel, an analyst frequently encounters situations where data imported or entered as text needs to be converted into numeric values for calculations, sorting, or further analysis. The VLOOKUP function is not used for conversion; it retrieves data from a table based on a lookup value. The COUNT function tallies numeric cells but does not perform conversion. Conditional formatting changes visual appearance based on rules but leaves the underlying data type unchanged. Absolute cell reference ($ signs) fixes a cell address in formulas and has no role in changing text to numbers. Although none of the options is a perfect direct tool for text-to-numeric conversion (common methods include VALUE(), Paste Special as Values, or Text to Columns), VLOOKUP is the only option that can be creatively combined with helper columns or lookup tables to map text categories to assigned numeric codes, effectively converting text labels into usable numbers during data preparation.
An analyst working in Google Sheets has converted data in a data set to a numeric value. Which function does the analyst use to determine that the data has been effectively converted?
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VLOOKUP
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IFNA
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SUM
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TRIM
Explanation
Explanation
Correct answer: (C.) SUM
To verify that data has been successfully converted to numeric values, an analyst can use the SUM function. If the data is truly numeric, the function will calculate a total without errors. If the data is still stored as text, the SUM function will either ignore those values or return an incorrect result, signaling that the conversion was not effective. The other functions serve different purposes: VLOOKUP searches for values, IFNA handles errors, and TRIM removes extra spaces from text.
Which resource should the analyst use?
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rOpenSci
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R User Groups
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rLanguage
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Tidyverse
Explanation
Explanation
Correct answer: (D.) Tidyverse
The appropriate resource depends on what the analyst is trying to accomplish, but in general data analysis workflows in R, the most widely used and structured ecosystem for data manipulation, visualization, and transformation is the Tidyverse. It provides a consistent set of packages (such as dplyr, ggplot2, and tidyr) that streamline common data science tasks and are specifically designed for efficient data cleaning and analysis. While rOpenSci focuses on scientific data tools and R User Groups provide community support, and “rLanguage” is not a standard structured toolkit, Tidyverse is the most directly relevant analytical resource.
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